Systems and methods for predictive document coding using continuous active machine learning
US10692017B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | May 24, 2017 |
| Grant date | Jun 23, 2020 |
| Priority date | — |
| Expiry date | Feb 21, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for predictive document coding using continuous active machine learning are described herein. A method uses both a primary queue and a plurality of secondary queues, where each secondary queue is associated with a model for category of documents. The method also repeatedly classifies new batches selected from a large set of documents that have not been reviewed. The classification uses the plurality of models and updates the secondary queues from the best documents in the most recently classified batch. While the method transparently cycles through batches, the most relevant documents are provided to one or more human reviewers from secondary queues via a primary queue. The reviewer confirms relevance or non-relevance in each of the documents for each of the categories. Periodically all the models are retrained using the set of reviewed documents after a selectable number of documents have been reviewed since the most recent retraining.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.